See the Forest and the Trees: A Synergistic Reasoning Framework for Knowledge-Based Visual Question Answering

• URL: http://arxiv.org/abs/2507.17659v3
• Date: Wed, 13 Aug 2025 11:46:04 GMT
• Title: See the Forest and the Trees: A Synergistic Reasoning Framework for Knowledge-Based Visual Question Answering
• Abstract summary: We propose Synergos-VQA, a novel synergistic reasoning framework.<n>At its core, Synergos-VQA concurrently generates and fuses three complementary evidence streams at inference time.<n>Our approach demonstrates strong plug-and-play capabilities, significantly boosting various open-source MLLMs.
• Score: 15.983971611104057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have pushed the frontiers of Knowledge-Based Visual Question Answering (KBVQA), yet their reasoning is fundamentally bottlenecked by a reliance on uni-dimensional evidence. This "seeing only the trees, but not the forest" approach prevents robust, multi-faceted understanding. Inspired by the principle of seeing both the forest and trees, we propose Synergos-VQA, a novel synergistic reasoning framework. At its core, Synergos-VQA concurrently generates and fuses three complementary evidence streams at inference time: (1) Holistic Evidence to perceive the entire scene (the "forest"), (2) Structural Evidence from a prototype-driven module to identify key objects (the "trees"), and (3) Causal Evidence from a counterfactual probe to ensure the reasoning is robustly grounded. By synergistically fusing this multi-faceted evidence, our framework achieves a more comprehensive and reliable reasoning process. Extensive experiments show that Synergos-VQA decisively establishes a new state-of-the-art on three challenging benchmarks, including OK-VQA and A-OKVQA. Furthermore, our approach demonstrates strong plug-and-play capabilities, significantly boosting various open-source MLLMs and proving that superior methodological design can outperform sheer model scale.

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